Search Results (3,900)

Secure data collaboration among mutually distrustful organizations requires more than encrypted storage: it also needs accountable ownership control, auditable access governance, privacy-preserving transaction execution, and reliable settlement when data are exchanged as digital assets. This paper proposes TrustTrade, a unified multi-party secure data management and transaction framework designed for cross-organization data sharing, trading, and compliance-sensitive analytics. TrustTrade integrates policy-bound data capsules, a tamper-evident provenance ledger, adaptive threshold escrow, verifiable data-payment settlement, and selective audit with revocation rebinding. On four real-dataset-derived workloads, TrustTrade reaches a $90.4$–$94.8\%$ settlement rate, with a $92.5\%$ average that is $6.4$ percentage points higher than the strongest baseline average. Under adversarial request injection, TrustTrade reduces unauthorized release to $0.31\%$ and atomicity violation to $0.38\%$, corresponding to $93.6\%$ and $93.0\%$ reductions compared with Plain-Market, respectively; compared with Fixed-Escrow, unauthorized release is reduced by $77.4\%$. TrustTrade also achieves $96.7\%$ dispute-resolution accuracy while maintaining practical settlement latency. These results indicate that jointly designing secure data management and secure data transaction protocols offers a practical path toward trustworthy multi-party data ecosystems. Full article

Against the backdrop of carbon peaking, carbon neutrality, and digital economy development, exploring the pathways through which artificial intelligence (AI) applications in manufacturing enterprises empower green transformation is of great significance. Using panel data on Chinese A-share listed manufacturing companies from 2005 to 2024 and a difference-in-differences (DID) model, this study examined the impact of the National Artificial Intelligence Innovation and Application Pilot Zones (AI Pilot Zones) policy on corporate green innovation. The results showed that the establishment of AI Pilot Zones significantly promoted green innovation among manufacturing enterprises, and this conclusion remained robust after parallel trend tests, PSM-DID estimation, and alternative variable measurements. Mechanism analysis revealed that financing constraints served as a key mediating channel, and that AI policies promoted green innovation through a serial mediation mechanism involving fintech development and the alleviation of financing constraints. Moderation analysis indicated that both human capital and digital transformation enhanced the policy effect. Heterogeneity analysis suggested that the policy’s impact was more pronounced among non-state-owned enterprises, large enterprises, and firms located in eastern regions. This study provides empirical evidence on the effectiveness of AI Pilot Zones in promoting green innovation among manufacturing firms and clarifies the underlying mechanisms. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Against the backdrop of in-depth integration between the digital economy and green low-carbon development, exploring how digital technologies facilitate the systematic low-carbon transition of economy and society bears profound theoretical and practical implications for accomplishing China’s “Dual Carbon” goals. Based on provincial-level panel data covering 31 Chinese provinces over the period from 2015 to 2024, this paper adopts two-way fixed-effect specification, instrumental variable approach and Bootstrap-based mediation test to empirically identify the causal impact, underlying mechanisms and heterogeneous boundary conditions of digital technology adoption on low-carbon economic transition. The empirical results demonstrate three core findings. First, digital technology applications exert a statistically significant positive effect on low-carbon economic transition, and this benchmark result remains robust after a battery of robustness tests and endogenous bias corrections. Second, the mechanism estimation uncovers a sophisticated transmission pathway: digital technologies directly accelerate low-carbon transition, yet generate an adverse indirect impact via the green investment channel, which jointly forms a suppressing effect in the mediation framework. Third, the enabling effect of digital technologies on decarbonization presents striking regional imbalance, with significant promotional effects concentrated exclusively in eastern provinces and regions featuring well-developed marketization, which highlights the indispensable moderating role of regional endowments and institutional environments. This study contributes novel empirical evidence to unpack the intricate nexus between digital advancement and green transition, and delivers actionable policy references for designing differentiated and coordinated strategies to integrate digital upgrading with low-carbon development. Full article

Background: Rule-based multi-agent system (MAS) architectures for healthcare coordination rely on hardcoded decision trees that cannot generalise to novel clinical scenarios or self-correct reasoning errors. These limitations are acute in surgical continuum care, where patients traverse presurgical risk stratification, intraoperative monitoring, postsurgical ICU, ward care, and remote rehabilitation over days to weeks—a complexity no fixed-policy agent architecture can address without prohibitive rule engineering. Objective: We present the first agentic large language model (LLM) framework for autonomous end-to-end surgical continuum monitoring, superseding the prior rule-based MAS Digital Twin. Six ReAct-driven tool-use agents replace fixed-policy agents with dynamic reasoning, multi-hop evidence retrieval, and Reflexion self-correction while maintaining mandatory confidence-gated Human-in-the-Loop (HITL) gating at every care-pathway-modifying decision. Methods: The framework is grounded in the ReAct paradigm and Reflexion self-evaluation, embedded within the DETER Digital Twin state engine S(t). Each agent is specified by a ReAct loop signature, a ten-function clinical tool registry, and confidence-gated HITL escalation logic. Inter-agent coordination replaces the rule-based Priority Queue Manager with an LLM-mediated Coordination Supervisor Agent reasoning over competing resource requests. Results: The framework delivers: (i) six formally specified ReAct-loop agents with explicit tool registries and authorisation boundaries; (ii) a confidence-gated HITL architecture that reduces alert fatigue while preserving safety for ambiguous clinical scenarios; (iii) an extended conflict resolution function P(p,t,context) incorporating surgical phase and DETER deterioration trajectory gradient; (iv) Reflexion self-correction with a formal N_max = 2 termination condition and Clinical Factuality Verification Layer; and (v) a multi-phase Digital Twin state engine extending S(t) to the full surgical continuum. Conclusions: The proposed framework represents a fundamental architectural departure from rule-based clinical AI—from hardcoded policies to dynamic reasoning, from static retrieval to multi-hop tool-use chains, and from fixed escalation thresholds to confidence-gated self-evaluation—providing a formally specified, clinically deployable foundation for next-generation autonomous surgical care coordination. Full article

Based on panel data of Chinese A-share listed firms from 2011 to 2023 (29,868 firm-year observations in total), this paper explores the environmental spillover relationship between regional digital financial inclusion (a proxy for the external digital financial ecosystem) and corporate financial investment efficiency. To identify causal effects, we adopt firm fixed effects and three strategies to mitigate endogeneity, namely, interactive fixed effects, lagged terms of regional digital financial inclusion, and instrumental variable estimation. The results suggest that regional digital financial inclusion, when interpreted as an environmental spillover from the external digital financial ecosystem, is associated with curbed inefficient financial investment and thus with improved investment efficiency. This effect operates through three channels: easing financing constraints, improving managerial sentiment, and accelerating digital transformation. Moreover, the positive effect is statistically significant and concentrates among non-state-owned enterprises, firms in eastern China, and sectors with limited traditional financial access (e.g., manufacturing and low-contact industries). Different from prior studies focusing on real investment efficiency, this paper enriches the literature on regional digital financial inclusion from an environmental spillover perspective. It also offers policy implications for fostering sustainable economic growth, strengthening the resilience of the real economy, and improving capital allocation efficiency. Full article

As a core driver of high-quality urban–rural integration, digital inclusive finance plays a key role in the process of Chinese-style modernization. After measuring the level of high-quality urban–rural integration development using the TOPSIS entropy method, this study employs fixed-effects models and mediation models to empirically examine how digital inclusive finance influences high-quality urban–rural integration development over the period from 2012 to 2022. The main findings are as follows: (1) Digital inclusive finance has a significantly positive promoting effect on high-quality urban–rural integration. (2) The enabling effect of digital inclusive finance exhibits significant regional heterogeneity, following a gradient pattern of “strongest in the Eastern region, followed by the Central region, and weakest in the Western region.” (3) In terms of dimensional effects, the breadth of coverage contributes the most, followed by the depth of use, while the degree of digitalization has the smallest impact. (4) The mediation mechanism indicates that factor mobility indirectly promotes high-quality urban–rural integration. Based on the above findings, this paper proposes policy recommendations to foster high-quality urban–rural integration development in China. Full article

As sustainable finance continues to advance, an important question is how scientifically designed and well-targeted policies can curb corporate ESG greenwashing and improve the quality of firms’ ESG and sustainability disclosure. From the perspective of fiscal–financial policy coordination, we exploit the green loan interest subsidy policy (GLIS) as a quasi-natural experiment and develop an analytical framework around four policy components: commercial banks’ information screening, local governments’ green screening, the subsidy instrument’s leverage and certification effects, and firms’ internal green governance. Within this framework, we examine whether the GLIS can restrain corporate ESG greenwashing. Using Chinese listed firms from 2009 to 2022 as the sample and identifying the effect through a multi-period difference-in-differences (DID) model, we find that the GLIS significantly curbs corporate ESG greenwashing. In exploring the underlying channels, we find that the GLIS curbs corporate ESG greenwashing by strengthening commercial banks’ information screening, enhancing local governments’ green screening, easing firms’ external financing constraints, and reinforcing firms’ internal green governance. Further analysis indicates that the inhibitory effect of the GLIS on corporate ESG greenwashing is more pronounced among non-state-owned firms, firms in the growth stage, firms in heavily polluting industries, and firms located in regions with weaker resource endowments. In addition, the stronger a firm’s digital technology R&D capability and corporate governance capability, the greater the restraining effect of the GLIS on its ESG greenwashing. By systematically evaluating the governance effect of fiscal–financial policy coordination on corporate ESG greenwashing, our study provides useful insights for governments seeking to improve green finance policies and optimize the coordination of green policy instruments. Full article

Carbon reduction is an urgent challenge for developing nations that balance socioeconomic development and climate mitigation in global low-carbon control. As a key digital economy means, e-commerce development enables urban low-carbon transition. In this context, drawing on a Chinese panel dataset covering 283 cities during 2006–2022, and taking the National E-commerce Demonstration City Pilot Policy (NEDCP) as a quasi-natural experiment, we use a multi-stage difference-in-differences (DID) strategy to detect how NEDCP affects urban carbon emissions. The results reveal that the NEDCP greatly reduces carbon emissions at an urban scale, which remains robust through a series of robustness tests. Mechanism analysis focuses on three channels, which includes boosting energy efficiency, advancing the digital economy, and promoting green innovation. Heterogeneity tests show that these benefits are more strongly evident in cities with a higher openness, a larger population, better economic conditions, and a stronger innovation capacity. The spatial spillover effect test shows that the NEDCP not only promotes local carbon reduction, but also promotes carbon reduction in neighboring areas. These findings offer theoretical insights for enhancing the NEDCP’s environmental benefits, and a practical guide for differentiated low-carbon development strategies, especially for prioritizing logistics and innovation support and refining green e-commerce standards. Full article

Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence. Full article

Zoomafia is frequently invoked in Italian public, advocacy, and institutional discourse to describe profit-oriented animal-related crime, but the term remains analytically broad and insufficiently connected to criminological theory. This narrative criminological review examines zoomafia as a cautious social-scientific lens for studying organized animal-related crime across heterogeneous illicit markets. Keyword-driven searches in Scopus, Web of Science, PubMed, and targeted criminological, legal, policy, and institutional sources were complemented by citation tracking and qualitative source selection. Peer-reviewed scholarship forms the analytical core, while legal, institutional, and advocacy materials are used selectively and with explicit evidentiary limits. Findings suggest that organized animal-related crime is best understood through market governance, brokerage, legal-illegal interface management, digital mediation, logistics, facilitation, evidentiary visibility, and variable convergence with other illicit economies, rather than through generic offence labels alone. The Italian perspective is analytically useful because companion-animal trafficking, dog fighting and betting circuits, clandestine horse racing, illicit slaughtering, wildlife trafficking, and online-facilitated trade can be compared within a shared frame that also exposes the limits of rhetorical mafia labelling. The article argues that zoomafia should not be treated as a self-proving mafia label, a new legal category, or a synonym for wildlife trafficking, but as a comparative framework for identifying organizational features, enforcement constraints, and evidentiary thresholds. The evidence base remains stronger on strategic recommendations than on robust comparative evaluation of enforcement effectiveness. Full article

Against the backdrop of accelerating digitalization and intelligent transformation, intelligent manufacturing has emerged as a key driver of green transition in manufacturing. However, evidence on its effects and the mechanisms underlying corporate green innovation remains limited. Using panel data of Chinese A-share manufacturing firms from 2011 to 2023, this study exploits the pilot policy of intelligent manufacturing as a quasi-natural experiment and employs a difference-in-differences (DID) approach. Results indicate that intelligent manufacturing significantly enhances firms’ green innovation, with robust evidence across multiple checks. Mechanism analysis shows that this effect operates through an integrated dynamic capability channel, whereby firms strengthen their adaptive capability, absorptive capability for green knowledge and digital technologies, and innovation capability through technological integration, thereby improving green innovation. Moreover, intellectual property protection strengthens this mechanism by increasing innovation returns and enhancing the capability-to-innovation conversion efficiency. Heterogeneity results suggest stronger effects in non-high-tech firms, non–heavily polluting industries, and technology-intensive firms, reflecting differences in digital readiness and resource reconfiguration capacity. Overall, this study provides causal evidence on the green effects of intelligent manufacturing, clarifies internal mechanisms, and highlights institutional and firm-level heterogeneity, with implications for digital-driven green transformation and policy design. Full article

Digital financial services have expanded rapidly across emerging economies and are often presented as tools for advancing women’s economic inclusion. However, the extent to which digital finance is associated with lower gender inequality depends on the broader structural conditions in which women live and work. This study examines the relationship between digital financial participation, labor market integration, and gender inequality in Brazil using nationally representative microdata from the 2025 Global Findex survey. Three outcomes are examined: digital account ownership, use of any digital payment, and engagement in merchant digital payments. Multivariate logit models show moderate gender gaps at early stages of digital financial participation. However, these gaps are not uniform across the population. The interaction results show that gender differences are concentrated mainly among individuals outside employment and among those without internet access. Among employed and digitally connected individuals, the gender gap becomes small and statistically insignificant across the three outcomes. A nonlinear decomposition shows that observable socioeconomic characteristics explain only a small share of the aggregate gender gap, especially for account ownership and any digital payment use. Additional robustness checks using probit and complementary log-log models support the main pattern of results. This suggests that the gender gap cannot be explained only by differences in education, income, employment, or internet access, and may also reflect unobserved household, institutional, or social constraints. The findings suggest that digital finance alone does not equalize participation. Rather, women’s digital financial participation is closely associated with their position in the labor market and their access to digital infrastructure. Because the analysis is based on cross-sectional data, the results should be interpreted as conditional associations rather than causal effects. Digital financial expansion is therefore more likely to support gender inclusion when it is linked to broader policies that strengthen women’s labor force attachment, digital connectivity, and economic autonomy. Full article

This study examines whether the digital economy helps provincial economies sustain industrial structure upgrading under economic policy uncertainty (EPU), and whether regional AI orientation strengthens this role. Using a balanced panel of 30 Chinese provinces from 2015 to 2023, the study uses the standardised logarithm of a provincial digital economy index as its core measure of digital development. Province and year fixed-effects models show that the triple interaction among digital economy development, regional AI orientation, and high EPU is positive and statistically significant. Marginal effect analysis indicates that the digital economy effect under high EPU only becomes positive when regional AI orientation exceeds a threshold, suggesting a conditional rather than universal effect. Robustness checks, alternative dependent variables, province-grouped machine learning validation, and supplementary policy exposure evidence based on Broadband China pilots are consistent with this state-dependent complementarity, although the estimates are interpreted as conditional associations rather than definitive causal effects. Full article

Environmental information disclosure is a critical pathway for manufacturing enterprises to advance the modern environmental governance system. Using the New-Generation Artificial Intelligence Innovation and Development Pilot Zones (NAIDP) as a quasi-natural experiment, this study employs panel data of Chinese A-share listed manufacturing firms from 2011 to 2023 to investigate the effect and underlying mechanism of the NAIDP on corporate environmental information disclosure. The results indicate that the NAIDP significantly enhances enterprises’ environmental information disclosure, and this positive effect is more salient for non-state-owned, superior digital infrastructure, and non-heavy-pollution enterprises. Mechanism tests demonstrate that the NAIDP functions by mitigating information asymmetry and enhancing internal control. Further analysis of the moderating effect suggests that management’s environmental awareness and regional environmental regulation intensity positively strengthen the promotional effect of the NAIDP. This study not only supplements micro-level empirical evidence for the environmental governance effect of artificial intelligence applications but also provides practical insights for policy optimization to facilitate the green transformation of the manufacturing industry. Full article